Vijay Bhargava


Relevant Degree Programs

Affiliations to Research Centres, Institutes & Clusters


Graduate Student Supervision

Doctoral Student Supervision

Dissertations completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest dissertations.

Machine learning algorithms for intruder signal detection and device localization in wireless radio frequency systems (2022)

Radio frequency (RF) wireless systems generate large amounts of data every day on the signal content and the received signal strength (RSS) information. However, limited efforts have been put into analyzing the RF data for security applications, such as the detection of unauthorized transmissions from an intruder. To bridge this gap, the Ph.D. thesis presents machine learning algorithms which detect and localize the intruder devices from their RF transmissions.First, we study the problem of detecting the intruder RF signals in wideband RF traces. An example setup is considered, with the intruder transmitting Wi-Fi signals and there exists interference from Bluetooth and microwave oven signals. We show that the conventional energy-thresholding algorithms are sensitive to noise variations and they require handcrafted parameters for each RF trace. To address this concern, we develop a deep learning solution, which employs convolutional neural networks to perform the signal de- tection. Experiments on both synthetic and real RF traces confirm the superior performance of the proposed solution in terms of the achieved mean average precision.Second, we study the problem of locating the intruder device from the RSS measured passively in the system. We work with the difference of RSS (DRSS), calculated with respect to a reference sensor, in order to handle the unknown transmission power and device heterogeneity of the intruder. The localization problem is formulated as a Gaussian process regression (GP) task to obtain the location estimates and the associated confidence intervals in closed-form. We propose two GP methods which take the stochastic nature of the test DRSS into account and provide more accurate confidence intervals on the test locations than the conventional GP method.Third, to improve the localization accuracy of the proposed GP methods, we present data reconstruction techniques which exploit the low-dimensionality exhibited by the DRSS vectors. Fourth, we study intruder localization for the case when the rate of signal strength decay with distance, also called the path loss exponent, is Gaussian distributed in the area. We propose two low-cost linear least squares estimators for the device location, which employ multilateration on the maximum-likelihood estimates of the distances to the sensors.

View record

Pricing and resource allocation in edge computing (2020)

Edge computing (EC) has been proposed to complement the cloud to meet the soaring traffic demand and accommodate diverse requirements of various services and systems in futurenetworks. By distributing storage, computing, control, and networking functions closer to theedge, the emerging EC paradigm promises to deliver superior user experience and enable awide range of Internet of Things applications. Despite the tremendous potential, EC is still inits infancy stage and many interesting open problems remain to be solved. This thesis aims todevelop efficient algorithms for pricing, service placement, and resource allocation in EC.First, we consider the joint service placement, sizing, and workload allocation problemunder demand uncertainty from the perspective of a service provider (SP). Specifically, wepropose a novel two-stage adaptive robust optimization model to help the SP identify optimallocations for installing the service and the resource amount to purchase from each location.The optimal service placement and sizing solution can hedge against all possible realizationsof the traffic demand within an uncertainty set. Hence, it enables the SP to balance thetradeoff between the operating cost and the service quality while taking demand uncertaintyinto account. Furthermore, the proposed scheme is less conservative than the static robustapproach and more robust than the deterministic approach.Second, we introduce a new market-based framework for efficiently and fairly allocatingthe limited resources of geographically distributed heterogeneous edge nodes to competingservices with diverse requirements and preferences. By properly pricing edge resources, theproposed framework generates a market equilibrium solution that not only maximizes theedge resource utilization but also allocates favorite resource bundles to the services given theirbudget constraints. We show that the equilibrium allocation is Pareto-optimal and satisfiesdesired fairness properties including sharing incentive, proportionality, and envy-freeness. Wefurther generalize the market model to tackle the case of net profit maximization and to capture practical system design aspects such as multiple resource types and limited demand.Finally, we present distributed algorithms for equilibrium computation while respecting userprivacy. Numerical results demonstrate the superior performance of the proposed marketmodels compared to benchmark schemes.

View record

Resource Allocation in Cooperative and Heterogeneous Wireless Networks with Energy Harvesting (2018)

The number of wireless connected devices is increasing rapidly, owing to the increasing applications of Internet of Things (IoT) devices. To address the coverage and capacity demand in the future, the fifth generation (5G) network will have heterogeneous architecture with densely deployed small cells and relay nodes for cooperative communication. Since dense deployment of base stations and relay nodes incur high energy consumption, renewable energy harvesting is a promising technique of reducing non-renewable energy consumption. Meanwhile, with increasing demand of IoT applications, self-sustaining battery life is direly needed in low power sensor-like devices. Since installation of bulky renewable energy harvesting infrastructure is not feasible in such miniature sensor-like devices, wireless energy harvesting is another promising technique that enables self-sustaining battery life of such devices. In this thesis, we address the challenges of resource allocation in cooperative and heterogeneous wireless communication networks with renewable and wireless energy harvesting.First, we consider relay-based and user-based cooperation in uplink wireless-powered communication (WPC) to mitigate the ``doubly near-far" problem. We propose algorithms to jointly optimize resource allocation for downlink energy harvesting and uplink information transmission in uplink WPC network with relay-based and user-based cooperation. Our algorithms improve throughput performance of user equipments that are far from access point. Next, we address new challenges of interference management in heterogeneous networks (HetNets) when downlink simultaneous information and power transfer (SWIPT) is enabled in small cells. We jointly maximize energy harvesting rate and throughput of small cell users while keeping interference within tolerable level. In time-switching approach of SWIPT, we demonstrate significant improvement in the energy harvesting rate by enabling flexible interference tolerance in macrocell users. Finally, we address the conflict between maximization of throughput and minimization of power cost in HetNets with renewable energy harvesting. We propose different online and offline algorithms to determine dynamic base station activation policy jointly with downlink resource allocation to optimize the trade-off between throughput performance and the associated power cost. Our algorithms demonstrate significant increase in throughput and decrease in non-renewable power consumption when compared to the baseline schemes. Performances of the proposed algorithms are analyzed through numerical simulations.

View record

Physical Layer Security in Massive MIMO Systems (2016)

Massive multiple-input multiple-output (MIMO) is one of the key technologies for the emerging fifth generation (5G) wireless networks, and has the potential to tremendously improve spectral and energy efficiency with low-cost implementations. While massive MIMO systems have drawn great attention from both academia and industry, few efforts have been made on how the richness of the spatial dimensions offered by massive MIMO affects wireless security. As security is crucial in all wireless systems due to the broadcast nature of the wireless medium, in this thesis, we study how massive MIMO technology can be used to guarantee communication security in the presence of a passive multi-antenna eavesdropper. Our proposed massive MIMO system model incorporates relevant design choices and constraints such as time-division duplex (TDD), uplink training, pilot contamination, low-complexity signal processing, and low-cost hardware components. The thesis consists of three main parts.We first consider physical layer security for a massive MIMO system employing simple artificial noise (AN)-aided matched-filter (MF) precoding at the base station (BS). For both cases of perfect training and pilot contamination, we derive a tight analytical lower bound for the achievable ergodic secrecy rate, and an upper bound for the secrecy outage probability. Both bounds are expressed in closed form, providing an explicit relationship between all system parameters, offering significant insights for system design.We then generalize the work by comparing different types of linear data and AN precoders in a secure massive MIMO network. The system performance, in terms of the achievable ergodic secrecy rate is obtained in closed form. In addition, we propose a novel low-complexity data and AN precoding strategy based on a matrix polynomial expansion.Finally, we consider a more realistic system model by taking into account non-ideal hardware components. Based on a general hardware impairment model, we derive a lower bound for the ergodic secrecy rate achieved by each user when AN-aided MF precoding is employed at the BS. By exploiting the derived analytical bound, we investigate the impact of various system parameters on the secrecy rate and optimize both the uplink training pilots and AN precoder to maximize the secrecy rate.

View record

Resource Allocation in Wireless Systems with Relay-based Cooperation and Energy Harvesting (2016)

Wireless communication networks are subject to exponential growth as a result of proliferation of smart phones, diverse wireless services and Internet of Things (IoT) applications. This extensive growth of wireless networks can significantly increase energy consumption, and escalating environmental pollution and energy costs have already created an urge for green communication. Therefore, we need to be proactive in designing environment friendly communication technologies and efficient resource allocation solutions, which will potentially drive the future generation of wireless communication. In this thesis, we focus on two promising communication technologies, namely cooperative relaying, which improves energy and spectral efficiency by providing spatial diversity, and energy harvesting technology, which can improve sustainability by utilizing renewable energy sources. The objective of this thesis is to address a number of key challenges in the design of efficient resource allocation techniques for wireless systems based on these two communication technologies. Firstly, we address the problem of energy efficiency maximization for downlink orthogonal frequency division multiple access (OFDMA)-based cooperative networks. The power and subcarrier allocation policies are jointly optimized with quality of service (QoS) provisioning. Afterwards, we investigate frequency reuse in OFDMA device-to-device (D2D) cooperative systems in which D2D pairs are classified based on the level of proximity with each other. We propose different scenarios of downlink communications and provide efficient frequency allocation techniques. Moreover, resource allocation algorithms with low complexity and signaling overhead are developed. Next, we focus on energy limitation of the relay nodes in cooperative systems. Using wireless energy harvesting to power the relay nodes, we propose an efficient resource allocation algorithm. As wireless energy harvesting technology is only effective for charging small nodes in communication systems, finally, we focus on the issue of charging the wireless nodes with renewable energy. We investigate the problem of resource allocation in energy harvesting systems considering the fact that the energy harvested from environment may not be enough to satisfy the QoS of all users due to its random nature. Two different utility functions are introduced and both offline and online schemes are devised to address this problem.

View record

Transceiver Optimization for Broadband Cooperative Wireless Communication Systems (2016)

Cooperative broadband communications is a promising technique to improve the reliability, throughput, and coverage of the next generation wireless communication systems. Single-carrier transmission with frequency-domain equalization (SC-FDE) and orthogonal frequency-division multiplexing (OFDM) are the prevailing block based broadband schemes widely adopted in major wireless standards. Traditionally, these broadband schemes are deployed for point-to-point communications without the cooperation of any intermediate transmission nodes. However, as the communication systems evolve, both service providers and users are demanding higher data rates and non-seamless connection over large areas. As a result, it is necessary to design novel transceiver architectures that meet these stringent requirements. This dissertation proposes four such cooperative transceiver designs which are tailored for different communication scenarios. Firstly, for single-user SC-FDE broadband systems with multiple multi-antenna amplify-and-forward (AF) relays, we optimize the relay beamforming (rBF) filters and destination equalization filter based on the minimum mean-square error (MSE) criterion under an aggregate relay transmit power constraint. We also propose suboptimal rBF schemes which perform close to the optimal rBF scheme. Subsequently, we investigate single-user SC-FDE broadband MIMO AF relay systems. By exploiting the properties of the block-circulant channel matrices and the majorization theory, the problem is transformed into an equivalent power optimization problem with scalar variables. An alternating optimization algorithm is devised to obtain the optimal solution for the source and relay power allocation. Thirdly, we study a robust transceiver design for multiuser broadband systems with multiple single-antenna AF relays and in the presence of channel estimation errors. Our proposed design treats multiuser SC-FDE and OFDM based systems in a unified manner, where the goal is to maximize the network ABR subject to different types of relay power constraints. Lastly, we propose a robust transceiver design for single-user SC-FDE based multi-hop full-duplex decode-and-forward relay systems. The optimization problem is formulated as the minimization of the sum MSE or maximum MSE of different hops which takes into account the loopback interference of the full-duplex relays. We propose two algorithms to solve the resulting non-convex power allocation problems based on sequential geometric programming and alternating optimization, respectively.

View record

Cooperative Beamforming for Cognitive Radio Systems in Presence of Asynchronous Interference (2015)

Cognitive radio (CR) is considered a key enabling technology to exploit the underutilized and non-utilized radio spectrum bands. On the other hand, cooperative communication among nodes in CR networks, can improve the overall performance of CR systems, in terms of increasing data rates, attainable coverage range and overall energy efficiency, providing some diversity against shadow fading, and having low deployment costs. In a CR network, cooperative transmit beamforming can be achieved via a number of single antenna-based CR nodes organizing themselves in a virtual antenna array and focusing their transmission in the direction of intended CR receiver. However, deploying the beamforming in such a cooperative manner faces several implementation challenges. Therefore, in this thesis, we tackle some of these critical challenges facing the design and implementation of cooperative CR networks. The first challenge is referred to as asynchronous interference, that results from asynchronous arrival of the same signal from the set of cooperating CR nodes at primary receivers. Next, we address the problem of feedback overhead needed for cooperative beamforming. Specifically, each cooperating CR node requires knowing global information including other nodes' locations, in addition to accurate and instantaneous knowledge of their channel state information (CSI). We also tackle the problem of imperfect CSI estimation. Another important aspect of implementing cooperative CR networks is studied in this thesis, which is described as follows. Since the cooperating nodes can be located in different locations, they contribute differently to received signals at the CR receivers, as well as to interference signals at the primary receivers. Therefore, we propose different cooperating CR node selection strategies, to be applied in conjunction with cooperative beamforming. Finally, we study different participation decision making strategies that enable each CR user to independently decide whether to participate in the cooperative transmission or not, based on an offered incentive for cooperation and estimated cost of participation in cooperative transmission represented in transmit power. At the end of each chapter, we present some numerical examples to show the implications of ignoring different implementation challenges in the design of cooperative CR networks, and to assess the performance of the proposed solutions.

View record

Energy-Efficient Resource Allocation and Cooperation in Wireless Heterogeneous Networks (2015)

The deluge of mobile data demands a drastic increase of wireless network capacity. A heterogeneous network design, in which small cells are densely deployed, is required to satisfy this demand. However, it is critical that this dense deployment does not lead to a surge in energy cost. The aim of this thesis is to design energy-efficient resource allocation methods and explore the value of cooperation in terms of energy cost. In particular, three different cooperation schemes are studied. First, a multi-cell coordination scheme is proposed for maximizing the energy efficiency of heterogeneous networks. Although this problem is not convex, convergent algorithms are devised to find an efficient power allocation. We found that this simple coordination can offer a significant energy efficiency gain even in dense networks. Second, a joint energy allocation and energy cooperation is proposed for heterogeneous networks with hybrid power sources and energy storage systems. For this study, an offline optimization problem is considered, in which the cells allocate their energy over time based on average rate contraints, the changing channel conditions and the fluctuating energy arrivals. It is found that an optimal use of the harvested energy significantly improves the energy efficiency. A much larger gain is obtained when energy cooperation is also leveraged, i.e. when the cells can exchange their harvested energy through a smart-grid infrastructure. Third, the trade-off between energy cost and performance is addressed for cooperative clustered small-cell networks. In this cooperative model, the small-cell base stations form a cluster of distributed antennas to collectively serve their mobile users. Hence, a joint optimization of cell clustering and cooperative beamforming is proposed to minimize the total energy cost while satisfying the users’ quality of service. The problem is formulated as a mixed-integer convex program and solved with a decomposition method. For a given clustering, a distributed beamforming algorithm is also designed to achieve near-optimal performance at a small cost of signaling overhead. Through simulations, it is shown that these algorithms converge fast and enable the cooperative small cells to save valuable energy.

View record

Resource Optimization in Relay Based Cooperative Wireless Systems under Channel Uncertainty (2015)

Over the last decade, the demand for wireless resources has been rising exponentially with the increase of the number of new users and services. The primary objective of wireless communication research is finding solutions to meet this increasing demand with limited radio resources. Relay based cooperative systems greatly enhance the performance of resource-constrained wireless networks. By allowing cooperation via relays, it is possible to improve the transmission quality and energy efficiency and get similar benefits to those of multiple-input-multiple-output (MIMO) systems.However, the benefits of a relay based cooperation often depend on the channel state information (CSI) between various nodes. The CSI of such multi-hop systems is often imperfect and outdated due to channel fluctuations, limited feedback capacity, and channel estimation and quantization errors. To maximize the utilization of the radio resources, it is imperative to devise optimal resource allocation schemes that are robust under imperfect CSI. In this thesis, we consider different resource optimization problems for relay based cooperative systems and propose solutions that are robust and computationally efficient.First, we develop power allocation and relay selection schemes for a decode-and-forward (DF) cooperative wireless network under bounded channel uncertainty. We propose worst case optimization based approach to provide guaranteed quality-of-service (QoS) to the users. Next, we design a joint power allocation and admission control algorithm considering channel estimation error as unbounded Gaussian random variable. We propose a probabilistically-constrained optimization approach for QoS provisioning in slow fading channels. Finally, we propose to utilize user cooperation technique to establish communication among the secondary users (SUs) of a cognitive radio network (CRN). Power allocation and relay selection schemes that maximize the system goodput of the CRN are developed. We provide QoS to the SUs while satisfying the interference constraints of the primary user bands considering different channel uncertainty models. Numerical results demonstrate the effectiveness of our proposed schemes and implications of ignoring channel estimation error while designing resource optimization algorithms. Results reveal that the effects of ignoring the imperfectness among different channels are violations of QoS and interference constraints, which ultimately result in transmission failures and wastage of transmission power.

View record

Analysis and Design of BICM-OFDM and Buffer-Aided Relaying (2014)

The growing demand for high data rates, reliability, network densification, and coverage extension will make relaying one of the key enabling technologies in future wireless cellular and broadband access networks. As the underlying channels in broadband wireless communication experience frequency-selective fading, it is necessary to study new relaying schemes to exploit the inherent diversity offered bythe frequency-selective channels. We adopt the combination of bit-interleaved codedmodulation (BICM) and orthogonal frequency division multiplexing (OFDM) to combat frequency-selective fading. We propose and analyze several new half-duplex (HD) BICM-OFDM relaying schemes for single- and multi-source communication systems. We derive the asymptotic pairwise error probability (PEP) and show that the proposed schemes can successfully extract full space and frequency diversity offered by the channel. The PEP expressions are exploited to develop guidelines for system design such as sub-carrier allocation, relay selection, relay grouping, relay placement, and power allocation. Although conventional HD relaying schemes, where relays receive and transmit according to a pre-fixed schedule, are simple in operation, their performance can be limited because the best links may not be exploited, in particular, when thechannel changes from one time slot to the next. To circumvent this problem, we present buffer-aided relaying protocols where the relay decides on its transmission and reception based on the instantaneous qualities of the source-relay and relay-destination channels. For both flat-fading and frequency-selective fading links, wepropose link selection protocols for buffer-aided relaying, which can yield a large coding and/or diversity gain advantage over conventional HD relaying for finite and infinite buffer sizes. We assume that the channel state information (CSI) can be outdated when link selection is performed and show that if both the instantaneous signal-to-noise ratio (SNR) and the reliability of the CSI estimates are incorporated into the link selection protocols, a lower error rate can be achieved compared to considering the SNR only. We introduce a decision threshold to the link selectionprotocols, which can be tuned to ensure buffer stability and trade error rate withdelay and/or throughput.

View record

Resource Allocation and Performance Evaluation in Heterogeneous and Relay-based Wireless Networks (2014)

In the last decade, mobile data demand has been exponentially growing. Telecommunication industry finds it increasingly difficult to cope with this exponential growth through conventional cellular networks with carefully planned high power macro base stations (BSs). Therefore, the densification of BSs through introduction of low power BSs has been considered for implementation. The combination of macro BSs and low power BSs such as pico and femto BSs as well as relay nodes is referred to as heterogeneous networks (HetNets). HetNets impose major technical challenges in implementation such as severe interference cases and imbalance of load among macro BSs and low power BSs. One problem that needs to be re-addressed in the context of HetNets is the cell association problem. Although centralized cell association schemes are important in realizing the potentials of HetNets, mobile operators are interested in distributed schemes in which network elements decide based on their local information. In this thesis, we consider distributed cell association algorithms with quality of service provisioning. First, we propose a unified cell association algorithm that is particularly designed for downlink. Next, we consider uplink to have a downlink and uplink aware cell association scheme. The performances of the proposed schemes are examined through numerical simulations.Cooperative relay-based communication combined with orthogonal frequency division multiplexing (OFDM) and its multi access variant, orthogonal frequency division multiple access (OFDMA) has gained an immense interest in the last decade. Among all the research topics in OFDM relay-based communication, analyzing the outage behavior has been an invariable concern to researchers. To analyze the outage behavior, most of the researchers ignore the correlation between OFDM subchannels, and also assume equal bit allocation on all the subchannels. In this thesis, we analyze the outage behavior of a three-node OFDM relay-based network when these two assumptions are relaxed. Next, we characterize the global outage probability of a multi-user single-relay OFDMA network. Finally, a network consisting of a cluster of source-destination pairs and a cluster of relays is considered where we propose a low complexity relay allocation scheme. The outage analyses and the relay allocation scheme are examined through numerical simulations.

View record

Adaptive and efficient resource management for emerging wireless networks (2013)

Recent unprecedented growth in the wireless market has forced network operators to find new techniques to reduce the operating costs, increase data rates, improve the spectrum utilization and reduce the energy consumption of various network elements. To overcome these challenges, opportunistic spectrum access through cognitive radios and relay-based cooperative communications have emerged as a new communication paradigm. However, to make these technologies practical, various resource management techniques must be optimized. Furthermore, we also need to explore the energy efficiency of these next generation wireless systems and identify key research issues and challenges in order to achieve sustainable "green" communication networks.In this thesis, we design efficient resource allocation techniques for cognitive and cooperative networks and explore the energy efficiency of these systems. First, we study a capacity-maximizing power allocation problem in orthogonal frequency-division multiplexing (OFDM) based cognitive radio system while considering the allowed interference limits. We resort to an energy-aware capacity expression taking into account subcarrier availability and propose several suboptimal schemes that perform at a level close to that of optimal schemes. Second, we explore joint power and subcarrier allocation algorithms and fairness for OFDMA-based multiuser cooperative wireless systems. We challenge the traditional view of relaying algorithms by relaying only if it is beneficial. We state the problem in the form of a capacity-maximizing integer programming optimization problem and propose a heuristic solution. Next, we study the problem of relay selection in a cooperative network where regular mobile nodes could act as relays and cooperate if provided with incentives to do so. Using concepts from economics of asymmetric information, we propose incentive compatible schemes for relays and suggest a low-complexity heuristic relay selection scheme. Finally, we investigate the energy efficiency of the next generation wireless systems and present a short summary of methods and techniques to improve the energy efficiency of cellular networks. We also describe some important research issues and examine the major challenges to reduce the energy consumption of the cognitive and cooperative based emerging wireless networks. In concluding remarks, we also give some future research directions towards which the research in this thesis could lead.

View record

MAC protocol design and resource management for distributed cognitive radio networks (2013)

Cognitive radio (CR) has drawn extensive attention as a promising technique to enable dynamic spectrum allocation in wireless networks in order to increase spectrum utilization. Since coordination and communication over wireless medium is mainly performed at medium access control (MAC) layer, design of a smart MAC is vital for successful deployment of distributed CR network (DCRN). In this thesis, we first investigate research challenges specific to DCRN MAC design and present an overview of current state-of-the-art DCRN MAC protocols. We then propose a MAC protocol called OMC-MAC to address major research issues in DCRN. The distributed architecture and uncertainty in resource availability make QoS provisioning a challenging problem in such networks. Therefore, we incorporate a QoS support module in OMC-MAC in order to provide QoS guarantee to delay sensitive applications. We also investigate adaptive admission control mechanism to limit QoS users in DCRN. Next, we propose resource allocation schemes based on cross-layer interaction between MAC and network layers to minimize network wide resource wastage in multi-hop DCRN. In multi-hop DCRN, loss of a packet after traveling some hops results in wasting all resources allocated to it in previous hops. We propose schemes to allocate transmit power favoring packets which have traveled more hops before reaching resource allocating node. Similar resource allocation schemes are also investigated for multi-hop distributed orthogonal frequency division multiple access network. Finally, we explore research challenges and potential solution approaches in achieving virtualized future generation cellular networks. Network virtualization (NV) has been envisioned as a promising approach to reshape future generation wireless networks by enabling coexistence of several heterogeneous virtual networks to efficiently share the same physical resources and infrastructure. Successful implementation of NV in cellular systems largely depends on virtualization of their radio access part. Therefore, we investigate research issues in virtualizing radio access of cellular systems. We also discuss the potential of MAC mechanism and resource management developed in this thesis in enabling NV.

View record

Resource allocation schemes for next generation wireless communication systems (2013)

Recent studies have indicated that spectrum scarcity in next generation wireless networks is inevitable due to the surge of mobile communication usage which offers the capability of instant access to users’ data and multimedia content anywhere anytime. Demand of mobile data traffic is found to have doubled within a year, and is expected to grow by 15 times in about five years. Fulfilling such mounting demand with the limited radio resources is a huge challenge for next generation wireless communication systems. Moreover, meeting the diverse quality of service (QoS) requirements of various types of user applications is indispensable. To tackle these challenges and to improve the spectral efficiency, radio resource management needs to be optimized for various novel systems such as relay-based cooperative transmission technique and opportunistic spectral utilization using cognitive radio (CR) technology. In addition to spectral efficiency, effective resource allocation methods need to be designed to improve the energy efficiency of such systems to achieve sustainable green communication.In this thesis, we investigate performance of certain resource allocation techniques in next generation wireless communication systems through analytical modeling and propose improved solutions using results from these models and simulations. First, we analyze the performance of resource allocation schemes for guaranteed QoS provisioning in a relay-based cooperative communication system. We introduce novel methods for precoder design for multi-antenna source and relay stations employing joint zero-forcing method. We design various schemes to allocate available transmit power to the source and relay(s) in a spectrally efficient manner. Next, we study the performance of resource allocation schemes for multicarrier multiple-input multiple-output (MIMO)-based CR system. We propose an optimal power allocation scheme for such system considering the practical CR constraints. Finally, we investigate the issues and challenges in enabling green communication in next generation wireless communication systems. We propose energy-efficient resource allocation method for guaranteed QoS provisioning by employing relay-based cooperative communication. We also analyze the intrinsic trade-off between energy and spectral efficiency using multi-objective optimization approach. For all of the proposed algorithms and schemes, we also present extensive simulation-based results for comparison with methods existing in the literature.

View record

Advanced transceiver algorithm design for cognitive radio physical layer (2011)

With the ever increasing demand for wireless applications, current wireless systems are challenged to meet the higher data rate and higher reliability requirements. Although the current and future technological developments allow making these requirements reachable, some other resources remain limited. The radio spectrum is one such natural resource. Previous studies have shown that the radio spectrum is not efficiently utilized. Therefore, recent studies are focused on fully utilizing this unexpandable radio spectrum. Cognitive radio (CR) has emerged as a possible solution to improve the spectrum utilization by opportunistically exploiting the licenced users transmit spectrum in dynamically changing environments. On the other hand, the development of CR technology raises new challenges of proper design of transmission and receive schemes for CR to facilitate high data rate access and better performance along with high spectral efficiency. To achieve these objectives, in this thesis, advanced transceiver algorithms for CR physical layer are designed to improve the throughput and the error rate performance in hostile wireless channels.We first designed a linear precoder for orthogonal space-time block coded, orthogonal frequency division multiplexing (OFDM)-based multiple-input multiple-output antenna CR when operating in correlated Rayleigh fading channels. The linear precoder is designed by minimizing an upper bound on the average pairwise error probability, constrained to a set of per subcarrier power constraints at CR transmitter and a set of primary users interference power thresholds. An efficient algorithm is proposed to obtain the optimal precoder matrices. We then proposed a power allocation policy to achieve a lower-bound on the ergodic sum capacity of single-input single-output opportunistic spectrum sharing multiple access channel with imperfect channel estimates. An efficient algorithm is proposed to obtain the optimal power allocation for each CR transmitter. Finally, we proposed a blind parameter estimation algorithm for OFDM signal affected by a time-dispersive channel, carrier phase, timing offset, carrier frequency offset and additive Gaussian noise. The cyclostationarity properties of received OFDM signal in time-dispersive channel is exploited to estimate the OFDM parameters. These parameters includes OFDM symbol period, useful symbol period, cyclic prefix factor, number of subcarriers and carrier frequency offset.

View record

Cooperative spectrum sensing for cognitive radio networks (2011)

Radio spectrum is a very scarce and important resource for wireless communication systems. However, a recent study conducted by Federal Communications Commission (FCC) found that most of the currently allocated radio spectrum is not efficiently utilized by the licensed primary users. Granting opportunistic access of the spectrum to unlicensed secondary users has been suggested as a possible way to improve the utilization of the radio spectrum. Cognitive Radio (CR) is an emerging technology that would allow an unlicensed (cognitive) radio to sense and efficiently use any available spectrum at a given time. Reliable detection of the primary users is an important task for CR systems. Cooperation among a few sensors can offer significant gains in the performance of the CR spectrum sensing system by countering shadow-fading effects. In this thesis, we consider a parallel fusion based cooperative sensing network, in which the sensors send their sensing information to an access point, which makes the final decision regarding presence or absence of the primary signal. We assume that energy detection is used at each sensor. Presence of few malicious users sending false sensing data can severely degrade the performance of such a cooperative sensing system. In this thesis, we investigate schemes to identify malicious users based on outlier detection techniques. We take into consideration constraints imposed by the CR scenario, such as limited information about the primary signal propagation environment and small sensing data sample size. Considering partial knowledge of the primary user activity, we propose a novel method to identify malicious users. We further propose malicious user detection schemes that take into consideration the spatial location of the sensors. We then investigate efficient sensor allocation and quantization techniques for a CR network operating in multiple primary bands. We explore different methods to assign CR sensors to various primary bands. We then study efficient single-bit quantization schemes at the sensors. We show that the optimal quantization scheme is, in general, non-convex and propose a suboptimal solution based on a convex restriction of the original problem. We compare the performance of the proposed schemes using simulations.

View record

Dynamic Resource Allocation for OFDM-based Cognitive Radio Systems (2011)

Cognitive radio (CR) is an emerging technology that would improve spectrum utilization by exploiting unused spectrum in dynamically changing environments. We investigate the design of link adaptation algorithms (e.g., adaptive power and bit loading) for orthogonal frequency division multiplexing (OFDM)-based CR systems. Different power and bit loading schemes can be designed for CR users which exploits the time varying nature of fading gains across the OFDM subcarriers. However, one of the challenges here is to ensure that the interference caused to the primary users (PUs) remains below the target interference threshold. Therefore, not only do we need to consider the fading gains, but also the spectral distance of the subcarriers from the PU's band. In this thesis, we propose an optimal power loading algorithm, assuming that the rate can be varied continuously, for an OFDM-based CR system. The downlink transmission capacity of the CR user is thereby maximized, while the interference introduced to the PU remains within a tolerable range. We investigate the case of discrete (or integer) rate adaptation. A sub-optimal scheme for integer bit loading is presented which approximates the optimal continuous rate value to a nearest integer. Next, we propose schemes that maximize the capacity of CR users when only imperfect channel state information (CSI) is available at the CR transmitter while guaranteeing the statistical interference constraint. Further, we propose resource allocation schemes for a multiuser scenario where power is loaded for CR users not only in the subcarriers where PU is not present (overlay fashion) but also in the subcarriers where PU is present (underlay fashion). Finally, for the scenarios where the link between CR source and destination might be weak and not reliable for communication, we employ relays and propose relay and power allocation schemes. Numerical results have been presented for all the proposed algorithms.

View record

Radio Resource Allocation in Emerging Broadband Wireless Access Networks: Some Analytical Models and Their Applications (2010)

New generation wireless networks are designed not only to carry voice but also to support data-intensive and multimedia applications. Broadband wireless networks offer high bandwidth necessary to support these applications. However, without proper resource allocation schemes, increased bandwidth is not sufficient to meet diverse application quality of service (QoS) requirements. In designing or deploying a resource allocation scheme, it is crucial to understand the inter-relationship of the resource allocation scheme and important system parameters with resulting QoS performance. Analytical models provide an opportunity to derive these relationships in an accurate and readily verifiable way. In this thesis, we develop novel analytical models for radio resource allocation schemes in emerging broadband wireless access networks. These models are then adopted for in-depth analysis of QoS performance of the modeled schemes and in devising new solutions based on the models to either improve upon or complement those schemes. Our work primarily deals with Medium Access Control layer; however, in most of our contributions, we also consider cross-layer issues. First, we develop a queueing model for a downlink packet scheduling policy in IEEE 802.16e mobile broadband systems and propose a resource allocation framework based on this model. Compared to existing schemes, proposed framework offers a simple yet more effective way to provide QoS to a heterogeneous mix of applications. Second, we develop a cross-layer model for a prominent multiuser scheduling scheme in multi-antenna-based broadband cellular systems. It captures cross-layer effects of important parameters of the multi-antenna physical layer. The model output is shown to have important applications in QoS provisioning. Next, we perform queueing analysis of controlled channel access mechanism in IEEE 802.11e-based Wireless Local Area Networks. Using the insight gained, we propose a novel channel access scheduling mechanism that achieves very robust performance in meeting QoS guarantees. Finally, we focus on a promising new technology called Cognitive Radio (CR), which can greatly improve spectrum utilization in next generation broadband systems. We develop a queueing model to analyze the performance of an opportunistic spectrum access mechanism in CR networks. The model has important applications including cross-layer analysis and admission control in CR-based broadband networks.

View record

Secure and efficient wireless ad hoc networking (2008)

Wireless ad hoc networks have been emerged to support applications, in which it is required/desired to have wireless ommunications among a variety of devices without relying on any infrastructure or central managements. In ad hoc networks, wireless devices, simply called nodes, have limited transmission range. Therefore, each node can directly communicate with only thosewithin its transmission range and requires other nodes to act as routers in orderto communicate with out-of-range estinations. One of the fundamentaloperations in ad hoc networks is broadcasting, where a node sends a messageto all other nodes in the network. This can be achieved through flooding, in which every node transmits the first copy of the received message. However, flooding can impose a large number of redundant transmissions, whichcan result in significant waste of constrained resources such as bandwidthand battery power. One of the contributions of this work is to propose efficientbroadcast algorithms which can significantly reduce the number of redundant transmissions. We also consider some of the security issues of ad hoc networks. In particular, we carefully analyze the effect of the wormholeattack, which is one of the most severe threats against ad hoc networks. We also propose a countermeasure, which is an improvement over the existing timing-based solutions against the wormhole attack. Finally, in the last chapter, we propose novel point compression techniques which can be used in Elliptic Curve Cryptography (ECC). ECC can provide the same level ofsecurity as other public key cryptosystems (such as RSA) with substantially smaller key sizes. Smaller keys can result in smaller system parameters, bandwidth savings, faster implementations and lower power consumption.These advantages make ECC interesting for ad hoc networks with restricted devices.

View record

Master's Student Supervision

Theses completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest theses.

Joint resource management and pricing in edge computing (2022)

Edge computing (EC) has emerged as a vital technology that works in tandem with the cloud toreduce network traffic and enhance user experience by distributing computational and storageresources closer to end-users and data sources. Despite the tremendous advancements made inEC technology and the enormous potential it holds, it is still in its infancy stage with numerousopen challenges to overcome. In this thesis, we particularly aim to design efficient algorithmsfor pricing, service placement, resource management, and workload allocation in EC. While considering the joint resource management and pricing problem in EC, we take intoaccount the preferences of the services. Specifically, we propose a novel bi-level optimizationframework to assist the EC platform to determine the optimal edge resource prices not onlyto maximize its profit, but also help each service find an optimal resource procurement andworkload allocation solution to minimize its cost while improving the user experience. Whenthere is a single edge node (EN), we derive a simple analytic solution for the underlying problem.However, for general case with multiple ENs, the follower problem becomes sophisticated.To this end, we develop two efficient approaches based on the Karush-Kuhn-Tucker (KKT)conditions and linear programming duality, respectively, combined with a series of linearizationtechniques to optimally solve the underlying bi-level optimization problem. The proposedoptimal solution will maximize the profit of the EC platform and improve the edge resource utilization while minimizing the cost of every service. Numerical results demonstrate the superiorperformance of the proposed dynamic pricing scheme. When the services need to pay for the service placement costs, the follower problems contain integer variables, which results in an extremely challenging bi-level mixed integer optimization problem. Due to the non-convex lower-level problems, we cannot use the KKT or duality-based approach to transform each lower problem equivalently into a set of linear constraints. Inspired by the column-and-constraint generation method from the adaptive robust optimization literature, we design an iterative algorithm to find an exact optimal solution to the formulated bi-level integer optimization problem.

View record

Edge service placement and workload allocation under uncertainty (2021)

Edge computing (EC) has emerged as a promising architecture for hosting critical services with stringent latency and performance requirements, challenging to address in traditional cloud computing (CC) systems. EC makes distributed computation and storage resources close to end-users, providing low-latency and high-capacity services. Notable use cases of EC include real-time data analytic services, manufacturing automation, and computational offloading for the Internet of Things. Despite the tremendous potential, EC is still in its infancy stage, and many open problems remain to be solved. This thesis lies in the intersection of operations research and network economics, with a specific focus on developing mathematical models for decision-making and economic analysis of edge-cloud network systems. To support rapid response to incidents in EC, we propose a novel resilience-aware edge service placement and workload allocation model that jointly captures the uncertainty of resource demand and node failures. The salient feature of the proposed model identifies the optimal placement and procurement solutions that can hedge against all uncertain realizations of the traffic demand within an uncertainty set. Hence, it enables service providers to balance the trade-off between the operating cost and service quality while considering demand uncertainty and node failures. Furthermore, by leveraging the column-and-constraint generation (CCG) method, we introduce two iterative algorithms that can converge to an exact optimal solution within a finite number of iterations. We further suggest an affine decision rule (ADR) approximation approach for solving large-scale problem instances in a reasonable time. Extensive numerical results then demonstrate the advantages of the proposed model and solutions.

View record

A hybrid precoding and signal detection framework for future wireless systems (2019)

With energy efficiency and spectrum management being major concerns in future wireless systems, this thesis primarily focuses on the precoding and signal detection capabilities of next-generation wireless transceivers. In the first part of the thesis, we present a parallel framework to make hybrid precoding competitive in fast-fading environments. To enumerate, (i) a low-complexity algorithm which exploits the block diagonal phase-only nature of the analog precoder in a partially connected structure is proposed to arrive at a hybrid precoding solution for a multi-carrier single-user system using orthogonal frequency division multiplexing (OFDM), (ii) the original problem is broken down into independent subproblems of finding the magnitude and the phase components which are solvable in parallel, (iii) a per-RF chain power constraint is introduced instead of the sum power constraint over all antennas, which is much more practical in real systems, (iv) an alternating version of this scheme is proposed for increased spectral-efficiency gains, (v) wideband PCS architecture is critiqued for its applicability in future wireless systems and possible alternatives are discussed. In the second part of the thesis, we present a signal detection and time-frequency localization framework for smart transceivers. Although deep learning techniques for image analysis have been advancing at a breakneck pace for the past few years, their application to RF data has been relatively less explored. To enumerate our contributions, (i) we present a modification of an existing state-of-the-art object classification technique called Faster-RCNN (FRCNN) \cite{C108} for detection and time-frequency localization of the signal in a spectrogram of a wideband RF capture, (ii) insights into the design choices pertaining to the variables such as short-time Fourier transform (STFT) parameters, spectrogram and anchor sizes and network thresholds are discussed, (iii) synthetic data as per the recently proposed WiFi High Throughput (WiFi-HT) protocol \cite{wifi_ht} is generated and a mean average precision (mAP) of up to 0.9 is achieved when the model is trained and tested on positive signal to noise ratio (SNR) values, (iv) certain drawbacks of the model with respect to low SNR levels and disparate signal sizes are brought to light and possible solutions are discussed.

View record

Improving security for future wireless networks through friendly jamming (2017)

As the number of connected devices and the importance of mobile communications continue to increase, a greater emphasis must be placed on security. Due to the broadcast nature of wireless communications, wireless networks are very exposed to eavesdropping. While this can be addressed above the physical layers using encryption, this still allows the attacker to receive the message and future work may allow decryption. Physical layer security is an approach to security which exploits the wireless channel to prevent the attacker from decoding the message. This thesis examines the use of friendly jamming, in which some nodes in a network broadcast white noise in order to degrade the channel between the legitimate transmitter and the eavesdropper. We address two problems related to the use of friendly jamming to improve physical layer security. The first problem is routing a signal through a network while using the remaining nodes as jammers to secure the signal. This is solved as two convex problems of allocating power to the jammers and routing the signal using those jammers to secure the transmission. This is shown to be a feasible method to increase security in a network. The second problem is estimating the self-interference channel (SIC) without using a calibration period for full-duplex jamming receivers. As the transmitter cannot transmit while the receiver estimates its SIC using a half duplex pilot signal, eliminating the calibration period can represent a significant capacity gain. Estimating the channel while receiving the desired signal causes it to act as an additional noise source, but this is shown to be overcome through the use of long estimation times. Our proposed scheme is able to increase the secrecy capacity of the system over that of calibration based estimation.

View record

Massive MIMO for 5G Wireless Networks: An Energy Efficiency Perspective (2016)

As we progress towards the fifth generation (5G) of wireless networks, the bit-per-joule energy efficiency (EE) metric becomes an important design criterion because it allows for operation at practically affordable energy consumption levels. In this regard, one of the key technology enablers for 5G is the recently proposed massive multiple-input multiple-output (MIMO) technology, which is a special case of multiuser MIMO with an excess of base station (BS) antennas. However, techniques for extracting large EE gains from massive MIMO (MM) networks have not been actively investigated so far. We seek to address the above limitation in this thesis by (i) reviewing MM technology from an EE perspective, (ii) critically analyzing the state-of-the-art and proposing new research directions for EE-maximization in “hybrid MM” networks, where massive MIMO operates alongside other emerging 5G technologies, and (iii) proposing a novel resource allocation scheme for EE-maximization in MM networks. The thesis consists of three main parts.In the first part, we motivate the need for EE and explain why massive MIMO is promising as an energy-efficient technology enabler for 5G networks. In the second part, we critically analyze opportunities for EE-maximization in three types of hybrid MM networks, namely, millimeter wave based MM networks, MM-based heterogeneous networks, and energy har- vesting based MM networks. We analyze limitations in the state-of-the-art and propose several promising research directions which, if pursued, will immensely help network opera- tors in designing hybrid MM networks. In the third part, we propose a novel EE-maximization scheme which optimizes resource allocation in an MM network. Three communication resources, namely, the number of BS antennas, pilot power, and data power are optimized for EE. Since the optimization problem is difficult to solve in its original form, we propose a novel solution approach where each iteration solves a sequence of difference of convex programming subproblems. Simulation results render few interesting guidelines for network designers. For example, using higher pilot power than data power can improve the system EE, particularly when SNR is high. Also, the number of BS antennas should be optimized with the available power budget to ensure operation at peak EE.

View record

Interference in Wireless Mobile Networks (2015)

Given a set of positions for wireless nodes, the interference minimization problem is to assign a transmission radius (i.e., a power level) to each node such that the resulting communication graph is connected, while minimizing the maximum (respectively, average) interference. We consider the model introduced by von Rickenbach et al. (2005), in which each wireless node is represented by a point in Euclidean space on which is centered a transmis- sion range represented by a ball, and edges in the corresponding graph are symmetric. The problem is NP-complete in two or more dimensions (Buchin 2008) and no polynomial-time approximation algorithm is known. We show how to solve the problem efficiently in settings typical for wireless ad hoc networks. We show that if node positions are represented by a set P of n points selected uniformly and independently at random over a d-dimensional region, then the topology given by the closure of the Euclidean minimum spanning tree of P has O(log n) maximum interference, O(1) average inter- ference with high probability and O(1) expected average interference. This work is the first to examine average interference in random settings. We extend the first bound to a general class of communication graphs over a broad set of probability distributions. We present a local algorithm that constructs a graph from this class; this is the first local algorithm to provide an upper bound on expected maximum interference. To verify our results, we perform an empirical evaluation using synthetic as well as real world node placements.

View record

Scheduling and Power Allocation for Interference Mitigation in Heterogeneous Cellular Networks (2014)

The wireless industry is confronted with an exponentially increasing demand for ubiquitous wireless coverage and larger data rates. Recent studies have shown that the spectral efficiency of a point-to-point link in cellular networks has approached its theoretical limit. This demands an increase in the node density in order to further improve the network capacity. However, today's network already has dense deployments and high intercell interference severely limits the cell splitting gains. Moreover, high capital and operational expenditure associated further limit the deployment of high power macro nodes. In this thesis, we investigate on Heterogeneous Networks (HetNets), a new paradigm for increasing cellular capacity and coverage to meet the forecasted explosion of data traffic. HetNets consist of low power nodes such as pico and femto overlaid over a macrocell network. Nevertheless, the deployment of large number of small cells overlaying macrocells presents new technical challenges. We focus on interference management issues in HetNets and present user scheduling and power allocation schemes for interference mitigation. We investigate the performance of user scheduling and power allocation techniques for interference mitigation in HetNets. We present analytical modeling and propose improved solutions using results from the model and computer simulations. First, we propose a scheme to jointly minimize network outage probability and power consumption. Second, we propose a scheme to jointly maximize network throughput and minimize power consumption. Both these schemes guarantee Quality of Service (QoS) provisioning in HetNets. We analyze the intrinsic trade-off between network performance parameters, i.e., outage and power consumption; throughput and power consumption using multi-objective optimization approach. Different user scheduling schemes have been adopted such as best user selection, proportional fairness and round-robin. Thirdly, we also propose an energy efficient power allocation method and analyze its performance with guaranteed QoS provisioning. For all the proposed algorithms and schemes we provide extensive simulation based results.

View record

Reliable communication in cognitive radio networks (2013)

The emergence of new wireless applications has driven increased demand for radio spectrum and therefore, the fixed spectrum assignment approach cannot efficiently utilize the radio spectrum. On the other side, several researches show that there are many parts of the licensed spectrum bands that are left unused most of the time. To address the problem of limited spectrum resources and underutilization of the radio spectrum resource, cognitive radio was used to make it possible for secondary users to opportunistically access the underutilized radio spectrum bands. However, in comparison with the general wireless network, cognitive radio technology gives invaders more possibilities to influence the wireless networks. This makes it more challenging to guarantee reliable communication in cognitive radio networks. In this thesis, first we describe the cognitive radio, cognitive radio networks, and security threats in these networks. Then the concepts of spectrum sensing as well as cooperative spectrum sensing are presented. There are two important kinds of security threats in the cognitive radio networks, which have attracted considerable attention in the literature. The first kind of threat which is called primary user mimicry invasion includes the cognitive radios or some outsiders that try to emulate the primary user’s signal characteristics in order to interrupt the spectrum sensing process. In the second kind of invasion, known as spectrum sensing data distortion invasion, the disruption of the spectrum sensing process is caused by those cognitive radios that send false data to the fusion center. Primary user mimicry invasion and spectrum sensing data distortion invasion have been majorly focused on in the past researches. Considering the significance of these two kinds of security threats in the ways in which they have disturbing effects on the overall performance of the cognitive radio networks, I focus my research work on these two kinds of threats. Then I provide a survey of the state of the art detection and mitigation techniques against them. The shortcomings associated with some of these detection and mitigation techniques are also investigated, which can be used as starting points for future researches.

View record

Network coding based cooperative communications (2011)

Cooperative communications was proposed to enable spatial diversity in small and inexpensive devices. It allows the creation of virtual antenna array through the antennas of the participating users. The benefits offered by cooperation include increase in data rate, robustness against shadowing, decrease in overall transmit power of the system etc. However, when user cooperation is extended to include multiple users or multiple relays, the system suffers from loss in throughput due to increased number of channel use. To overcome this, cooperative communications schemes often make use of network coding which helps trade-off resource allocated for cooperation and system performance.In the first part of the thesis, we propose random network coding based user cooperation scheme in wireless networks. Our scheme is very effective in spreading the information of the pool of cooperating users so that the message can reach the destination via many alternative paths. Also, the proposed scheme is decentralized and the cooperating nodes act independent of the others. Results show that our scheme is resilient to inter-user channel noise and can achieve high diversity gain when number of cooperating users is large. We further enhance the performance of our scheme for bad user-destination channel by protecting the packets by convolutional coding. This version of our scheme performs better than traditional N user cooperation in terms of both outage and throughput for all user-destination channel conditions when inter-user channel is good. It also shows better robustness to inter-user channel than original scheme. In second part of this thesis, we consider analog network coding based bidirectional relaying system. We develop a scheme to optimally allocate power at the relay nodes such that overall data rate in transfer of messages between two user nodes is maximized under uncertain channel conditions. We have proposed an iterative solution for rate maximization problem and solve a geometric program at each step. Results show that bidirectional relaying can achieve significantly more data rate than conventional unidirectional relaying scheme at the cost of reduced diversity. Also, addition of more relays makes the system more robust to imperfections in channel.

View record

Power allocation schemes for cooperative communication system using weighted sum approach (2011)

This thesis investigates power allocation schemes for an amplify-and-forward dual-hop relay based cooperative communication system with perfect and imperfect channel state information (CSI). We define cost functions and propose power allocation schemes such that the cost functions are minimized. We analyze a multiuser system, where we select the best user for transmission, who incurs the least cost of transmission. In a practical system, estimated CSI is often imperfect. We assume the estimated CSI is affected by estimation errors, which are modeled as zero mean complex Gaussian. First we propose an optimization scheme where the objective is to minimize the weighted sum of source and relay powers. Then we propose a more general multi-objective optimization scheme which jointly optimizes sum power and signal-to-noise ratio (SNR). In our proposed schemes, source and relay nodes share a fixed total power, and transmission is allowed only if the minimum required SNR at the destination can be achieved with the available power budget. These schemes are analyzed under both perfect and imperfect CSI assumptions. In addition to proving the convexity of these problems, we propose analytical solutions for sum power minimization and SNR maximization schemes in the presence of imperfect CSI. Performance of the systems under the proposed schemes are investigated in terms of energy efficiency, throughput and outage. Simulation results show that proposed schemes reduce wastage of power by avoiding unsuccessful transmissions.

View record

Selective Subcarrier Pairing and Power Allocation for Decode-and-Forward OFDM Relay Systems with Perfect and Partial CSI (2010)

This thesis investigates a decode-and-forward two-hop relaying system consisting of one source, one relay and one destination, in which orthogonal frequency division multiplexing is used. The relay forwards the message received from the source on a subset of available subcarriers in the second time slot. Firstly, a subcarrier pairing and selection algorithm is proposed, assuming that perfect channel state information (CSI) is available at all nodes, then, power is allocated to both the source and relay stations under individual power constraints in order to maximize the capacity. Secondly, subcarrier selection and pairing, and power allocation (PA) under partial CSI assumption along with individual power constraints are addressed. The result is a novel distributed algorithm with low complexity maximizing the expected value of capacity at the source and relay nodes. Finally, the simulation results show that selective relaying combined with subcarrier pairing and PA improves the system capacity to a considerable extent in both perfect and partial CSI cases.

View record


If this is your researcher profile you can log in to the Faculty & Staff portal to update your details and provide recruitment preferences.


Read tips on applying, reference letters, statement of interest, reaching out to prospective supervisors, interviews and more in our Application Guide!